<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Dopazo</style></author><author><style face="normal" font="default" size="100%">Gordon, M. B.</style></author><author><style face="normal" font="default" size="100%">Perazzo, R.</style></author><author><style face="normal" font="default" size="100%">Risau-Gusman, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A model for the interaction of learning and evolution</style></title><secondary-title><style face="normal" font="default" size="100%">Bull Math Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms Alleles Animals *Evolution Genotype Humans *Learning *Neural Networks (Computer) Numerical Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer-Assisted Phenotype Synapses/genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11146879</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">63</style></volume><pages><style face="normal" font="default" size="100%">117-34</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a simple model in order to discuss the interaction of the genetic and behavioral systems throughout evolution. This considers a set of adaptive perceptrons in which some of their synapses can be updated through a learning process. This framework provides an extension of the well-known Hinton and Nowlan model by blending together some learning capability and other (rigid) genetic effects that contribute to the fitness. We find a halting effect in the evolutionary dynamics, in which the transcription of environmental data into genetic information is hindered by learning, instead of stimulated as is usually understood by the so-called Baldwin effect. The present results are discussed and compared with those reported in the literature. An interpretation is provided of the halting effect.</style></abstract><notes><style face="normal" font="default" size="100%">Dopazo, H Gordon, M B Perazzo, R Risau-Gusman, S Comparative Study Research Support, Non-U.S. Gov’t United States Bulletin of mathematical biology Bull Math Biol. 2001 Jan;63(1):117-34.</style></notes></record></records></xml>